Monday, 28 May 2012

An Evolutionary Model of the Brain's Internal Language

BRAINSTORM 11

The purpose of this post is to
suggest that the brain has a simple internal language for storing and using
memories of objects and events, that this language is common to animals and
humans, and as the capacity of the brain expands the language has sufficient
power to support human intelligence. In particular it identifies two areas where
changes in the learning/teaching mechanisms could have led to a tripping point
where it became economical (in evolutionary terms) to develop a larger brain. It
suggests that much, if not all, of what we consider intelligence is the
result of cultural factors.

The following topics are addressed

The Black Hole in Brain Research –
bridging the gap between the neuron level and intelligence.

Introducing CODIL - the history and references to this blue sky research project

Why CODIL is relevant - and some
factors to be considered

Evolving More Brain Power - instructing and programming cultural information

What Next? - Ensuring the ideas get followed up

1. The Black Hole in Brain Research

An unbelievable amount of very detailed research has been carried out world
wide on the inner workings of the brain, into the nature of natural language,
how children learn, intelligence in animals and humans, psychology and brain
disorders. However if one stands back and takes an overview there is no unified
model which allows one to follow a step by step route between the electrical impulses
at the neural level and, for example, how a child learns to talk or why we
enjoy Shakespeare. The purpose of this discussion document is to go back to
first principles and suggest a framework which, with further research could help to bridge this gap.

The approach taken is to start by assuming (1) that there are
a few basic information processing facilities which any animal capable of
modifying their behaviour as a result of learning must have, and (2) that the
human brain works in basically the same way – with no major changes – the
differences being mainly related to increases in size and the number of links.
In effect the approach suggests that a human is just a normal mammal except
that, like the giraffe and its neck, or the elephant and its trunk, it has
significantly expanded the way one particular organ, the brain, is used.

2. Evolutionary Pressure on Brain Size

It is very easy to assume that a bigger brain is always
better but in evolutionary terms this is not the case. Running a brain is an
additional (and non-trivial) expense and the more resources expended on the
brain the less resources are left for other needs and I have discussed some of
the factors in
Evolutionary Factors starting on the African Plains.

The problem is that running and “loading” the brain with
information has a cost, with everything learnt being lost at death. An animal
with a bigger brain will need to learn more to “fill it” with knowledge and this
will take time, possibly extending the childhood period. If there is any form of
“instruction by example” by parents this also has resource implications for the
parents. For herd animals it is advantageous for the young animals to play
“follow my leader” with the older animals. A young animal does not need to know
about predators if there are always older, more experienced animals there (not
necessarily of the same species) as long as they run when the herd runs. This
reduces the need for larger brains because vital information is automatically
shared with minimal “teaching effort” by the older animals.

This is developed further in social groups of primates and
elephants where there is a positive grandmother effect where the older, more
experienced animals can support the younger adults, such as a mother in labour
for the first time. Because information is passed positively between
generations, knowledge is not completely lost, so there could be advantages of a
more “intelligent” brain.

A critical factor is how much information can be
economically passed between generations – and with the coming of human language
(and later writing) there will be a need to evolve a means of faster learning to
reduce the learning and teaching times, and possibly a need for passing on
learning techniques. This is a factor which must be addressed in the current
model.

3. The Simple Brain Model

An animal which is capable of
learning must have a facility which will remember “objects” and recognise them
when they recur. For instance a chimpanzee must be able to recognise a banana,
and it would be useful if a mouse could recognise a cat. At this stage we will
not consider how this information is encoded in the brain but will simply refer
to the unit which stores and subsequently recognises real world items a
memode.

The job of the sense organs, and
the associated parts of the brain is to generate memodes. In a new born –
with an “empty” brain these memodes will be stored – and if the associated
pattern repeats the memode will be reinforced while the ones which do not repeat
will be lost. This will build up a language of “objects” recognised - and this
reflects what happens when human babies learn the phonemes that make up the
language of their parents.

In the real world the animal will
be aware of memodes relating to more than one object – and any effective learning will have to
relate how objects interact. In the current simple model the current context
consists of the most recently activated
memodes, which will record what the animal is currently seeing, smelling, etc.
At this stage the brain will need to link together memodes which are active at
the same time, using a similar learning pattern for the links – with common
links being reinforced and uncommon links being lost. Once this has happened the
network of linked memodes can start making predictions.

To take a
simple example where the linked memodes in a chimpanzee represents HUNGER –
BANANA – EAT. When the body activates the HUNGER memode the link, in effect,
interrogates the BANANA memode to see if it is true – asking “are you currently
seeing a banana?” If the answer is yes the link to the HUNGER memode is
triggered – and this in turn triggers the action of eating. If no banana is
currently being seen the BANANA memode will have been generated by the vision
section of the brain so could start a visual search for bananas. Such referrals
back to the originating sense areas of the brain could explain why, when
thinking about something, some people have a strong sense of seeing, hearing or
smelling what they are thinking about – and also relate to the imagery of
dreams.

How the memodes are linked

Let us
consider that a mouse might have memodes representing the SIGHT-OF-A-CAT, the
SMELL-OF-A-CAT and the SOUND-OF-A-CAT and while often these would occur together
they could also be actively singly. If they link together to form a new memode
CAT this could be “true” if any one of them was “true”, or is a combination of
them was “true”. When we get to thinking about humans and language one can
easily add in WORD-FOR-A-CAT as another way of identifying a cat.

But the
SIGHT-OF-A-CAT memode might similarly link to memodes representing the
EYE-OF-A-CAT, the WHISKERS-OF-A-CAT, the SLINKY-MOVEMENT-OF-A-CAT, etc. and
these in turn could have links to memodes concerned with even smaller details.
This demonstrates an important feature of the model. The memode network is
recursive, and, assuming that only some of the links at any stage have to be
true, provides a form of inexact (or fuzzy) matching. In humans, and I would
assume also animals, one is only aware to the highest level memodes. For
instance as a bird watcher I may instantly recognise one bird while being
unconscious of the individual features – but if there is a problem I will look
closely to see if I can identify an detail which will distinguish between
several possibilities.

A similar
approach can be used to classify hierarchies of different but related objects.
For instance the ANIMAL memode would link to CAT, DOG, ELEPHANT, etc, and the
CAT memode would linked to individual cats such as TIDDLES, GINGER, THOMAS, etc.
Memodes would be in several hierarchies and CAT might be in PET, BIRD_KILLER,
etc. as well as ANIMAL. The extent to how far animals can take this degree of
generalization is unclear to me, but it is only a variant of the previous
example involving the SIGHT, SMALL, and SOUND-OF-A-CAT.

There is one
other way in which memodes can be linked, and this is when very different things
are linked in a single event. A pet dog will link MASTER-STANDING, LEAD-IN_HAND
and WALK, and leap up, tail wagging in appreciation, when its master stands up
and picks up the lead. Some more intelligent dogs might even attempt to make
LEAD-IN-HAND true by picking up the lead and taking it to the master!

In many cases
a linked series of memodes such as these can be likened to a program statement
which interrogates the currently active memodes.

if MASTER-STANDING then if LEAD-IN-HAND then WALK

4. A more formal definition of a Memode

A memode is the active unit of information in the
brain. It represents an object, abstract concept or event which needs to be
remembered. While a memode might directly hold a low level piece of
information, it will often consist of links to other memodes. Any memode
can be highly active in the current context – which means that it is at
the focus of the brains attention. Otherwise, If triggered by another memode
it can compare itself with the memodes in the current context and
recognise if it matches. It can then either signal “true” back to the triggering
memode, join the current context, or trigger other nemodes.

Some memodes will be linked with the senses, while
some will be linked to the brain “outputs” - which will be triggered if the
memode enters the current context. In addition each memode will have
some kind of learning function associated with it, so that frequently used
memodes have a higher priority, while unused memodes eventually get forgotten.

In the brain a memode could be a single neuron or a linked
group of neurons.

This simple model
represents what can be considered a minimum for any animal which can learn to
recognise a reasonable number of real world entities and make simple decisions.
The key question to be asked is how far the human brain works in this way,
taking for granted that it has greater capacity in terms of the number of
neurons and the number of interconnections. If we start from the bottom up and
assume that there are no fundamental differences (apart from size factors)
between the animal model, described above, we will either end up showing that
there are no fundamental differences or we will be in a better position to
understand what they are. Studying the model may also suggest where the
main evolutionary pressures were.

5. Introducing CODIL

Fortunately
there was some research some years ago which used a similar model and while
there were differences, which will be discussed later, the experimental research
carried out then showed that such a simple approach could pack a lot of
processing punch.

CODIL started in the 1960s as a piece of blue sky research into alternative
information processing architectures which aimed to avoid some of the problems
inherent in the conventional stored program computer approach (see The Limitations of the Stored Program Computer). The underlying
idea arose from an unconventional approach to handling a very large commercial
task (Working
with LEO III Computers at SMBP)
and further research was actively supported by two of the pioneers of commercial
computing in the UK,
David Caminer and
John Pinkerton, The hope was to
provided a “white box” processor, which would allow humans work with it to
handle open ended tasks which could not easily be predefined in advance. CODIL
was, in effect, the human friendly symbolic assembly language for the proposed
new architecture. The basic principles were clearly demonstrated in
a series of publications
ending in a schools package which demonstrated its ability to handle a wide
range of artificial intelligence and other tasks which received
excellent reviews,
followed by a detailed paper in the top ranking
Computer Journal
. Unfortunately the project was aborted
before the final paper appeared in print for health related reasons.

While CODIL was conceived in terms a modified microcode “instruction set” within
the central computer processor, the research was carried out with four different
software simulations, and each was tested in different ways. The first was
simply a test bed to demonstrate that the idea would work. The second was
designed to test out a number of commercial data processing tasks on a small
scale, and was used to set up a
research medical data base and a
historical data base. There was
also a major excursion into
artificial intelligence with
TANTALIZE. In 1980 online access
became easier and the emphasis switched to using CODIL to
support teaching packages and to
demonstrate how it might support an interactive
publishing network (conceptually a
text ancestor of the world wide web). For various reasons the early software was
not very portable and with the coming of the the first small personal computers
it was decided to demonstrate that the idea was simple enough to fit into a BBC
Micro (effective memory 25Kbytes!!!) and the result was
MicroCODIL which prove logically
far more powerful than its predecessors and which attracted rave
reviews from a wide variety of
magazines. Unfortunately at the time the MicroCODIL software was being produced
there was a suicide that was a major factor leading to the closure of the
project in 1988. The final publication appeared in the
Computer Journal
in 1990.

6. Why CODIL is relevant

Computers were originally designed for highly repetitive numerical tasks
involving well designed pre-defined algorithms. The underlying mechanisms were
never designed to be human-friendly - and to most people the computer in
effectively a "magic black box." The more complex the task the more effort has
to be put into pre-defining the algorithms and it would be interesting to know
how many billions of man years (including time spent up blind alleys and on
training) have gone into creating the computer-dependent world we now live it.
While the benefits may be enormous, getting there has not been cheap.

CODIL starts from the opposite end of the spectrum. It effectively starts on
any task from a position of ignorance - with nothing pre-defined. It does not
formally break information down in to program and data, and there are no global
rules. In effect it works by comparing information which it has just received
with copies of information it has received in the past and uses this make simple
deductions, nothing more. The fact this works may surprise many familiar with
the limitations of the stored program computer, but many tests have shown a wide
variety of situations where it demonstrably works. The whole is set up so that
the human who is inputting the information can see into the "white box" so that
he can understand how the information is being handled.

The important thing is that CODIL was an attempt to provide a
flexible information tool which will help humans with complex open-ended logical
tasks which cannot be fully pre-defined in advance, and to do this it had to
model some aspects of how the human brain handles such problems. The detailed
working are described in the papers, cited earlier, but the key features are a
central “decison making unit” which scans linked lists of “items”,
comparing them with a list of items called the “Facts” which provides a
working short term memory. The principal difference in the underlying approach
is that in CODIL there is a single central processing unit while in the memode
model the decision making is distributed over the network.

There are many aspects of the CODIL research which could be
discussed in considerable detail at this stage to assess their relevance to the
memode model but to keep this post within a reasonable length I have selected
three where I feel that further work is required - and am very happy to provided
information on others on request.

CODIL was developed as a tool

The first thing to consider when assessing the relevance of
the result of the CODIL experiments is that CODIL was designed to be a practical
tool which could be used alongside conventional computer systems. It was
therefore taken for granted that, for example, any usable package should be able
to carry out a comprehensive range of mathematical calculations. It should also
be able to handle keyboard input and display what it is doing on a screen, or
route it to a printer. For this reason the experimental software packages
contained libraries of support routines which are not relevant to the brain
model. In the case of the package MicroCODIL about 95% of the software was there
to provide a dedicated text editing facility and about a dozen different windows
to show the user what the system was doing. (See for example
The Use of Colour in Language Syntax Analysis) Such presentation
features can safely be ignored in assessing the results of the trials with CODIL
and MicroCODIL. In addition counting, arithmetic, formal logic and the more
advance mathematical techniques are cultural features alien to our hunter gather
ancestors – and to all animals – and are not be considered as as part of the
basic memode model – although the ability to acquire such skills is relevant to
any model of the evolution of human intelligence.

Extending the Network

The CODIL network model was not designed with the memode
approach in mind and there are several ways in which it needs simplifying to
make it more like the the memode model of the brain. The first is related to
what could be considered the “bottom level” when actual information is stored.
This has been discussed in
From Neural Nets to Language which also discusses some of the ideas in this
post in more detail. The CODIL Item “MURDERER = MACBETH” effectively indicates
that MACBETH is a character string which is a member of the set MURDERER. For
the memode model this becomes a linked pair of memodes “MURDERER -> MACBETH”
where MACBETH is now also considered a set, which may have members. This can
then be extended to provide a broader model than CODIL normally supplies:

It also emphasises the need to do further research to
consider the specialist areas of the brain, such as the the parts that process
sight and sounds, as a continuation of the decision process, and a consideration
of the boundary between conscious and unconscious thought. There are also some
interesting aspects of the move between serial and parallel processing and the
problem of self referential problems.

CODIL as a
programming language

CODIL is a bottom up system which simply scans lists of lists
of lists of items and makes simply deductions, with the advantage that there is
no need for an explicit pre-defined global algorithm for the task. This means
that there is no formal distinction between items which can be used as “data”,
as “logic tests” or as “commands” - because any and every item (with a few
special cases concerned with input and output) can validly be used in any of
these roles. Exactly the same applies to the memode model.

But what happens if the person who wants to use CODIL has a
task which can be pre-defined? This is perfectly acceptable in that the user can
provide lists of items which he only wants to be used in a particular way – in
effect as a program. In the simplest case this “program” could just be a single
item to be interpreted as an instruction to start by comparing one list with
another – and the effect can be very far reaching because of the recursive
nature of the decision making process.

There are limitations to how far this approach can be taken
because CODIL uses associative addressing (unlike conventional computer systems
which use numbers for addressing purposes) and as such cannot easily handle
algorithms which are based on arrays, although a very limited facility has been
built in for handling items with multiple values. While most CODIL “program” are
very small the language was used to write one very significant “program” -
called
TANTALIZE.

TANTALIZE was designed to solve logic problems and was named
because it succeeded in solving 15 consecutive “Tantalizer”
problems (now renamed Enigma) as they were published weekly in the New Scientist
during the mid1970s. It was also used to solve many of the similar problems that
appears in the Artificial Intelligence literature at the time. Using the
terminology of the time TANTALIZE was a series of production rules, represented
in CODIL, which queried a human user about the logic problem to be solved. The
result was to generate a new series of production rules which were optimised
using a version of CODIL with a learning facility. The optimised version of the
rules were then “obeyed” to produce the correct logical solution in something
approaching the minimum number of steps.

The paper of TANTALIZE reported on how the approach compared
with other Artificial Intelligence research being carried out at the same time:

Compared with
other problem solvers, the TANTALIZE problem descriptions are almost always
simpler, the time to process problems is often more than an order of magnitude
less, and with a mere thousand [lines of
code] TANTALIZE provides an unrivalled range of heuristic search and
other facilities. Its comparative weakness in algebraic manipulation is more
than compensated for by the ease with which it can be extended by the use of
CODIL to carry out a wide range of operations.

There is no way that the complex set of rules such as those
that made up TANTALIZE could evolved on a memode network dependent on dynamic
trial and error learning – but the fact that the architecture can support
complex algorithms is important when we consider the evolution of human
intelligence. In particular there is the real possibility of generating English language statements from the "MACBETH" network illustrated above

7. Evolving more Brain Power

The basic memode model assumes that animals which can modify
their behaviour based on experience must have a brain that can (a) recognise and
remember objects in the real world, (b) link together objects which are
associated and (c) can use this information to make simple deductions. If we
then assume that the human brain is little more than a scaled up version of the
animal brain we need to ask what changes need to evolve in order to support the
the kind of intellectual activities humans are capable.

In increasing the “capacity” of the brain one could increase
the number of memodes, the number (and perhaps the nature) of the links between
them, the number of memodes (the Current Context) that can be active at one
time, and factors relating to learning and forgetting – some of which relates to
the depth of “recursion” which can be supported (i.e. how complex a single chain
of thought can be before it starts to forget).

The important thing to remember is that, in evolutionary
terms, there is no point in having a brain which has more capacity than is
needed, and one of the biggest costs will be that associated with learning. The
bigger the brain the longer the childhood learning period, the bigger the
investment the parents have to put into rearing and “teaching” the youngster,
and the more hard-earned knowledge that is lost when the brain dies. This
suggests that if one can reduce the cost of learning and/or the ability to
transfer significant amounts of knowledge from one generation to another, the
costs of having a larger brain fall while the benefits increase. If this
happened the bigger brain could make it easier to learn and transfer information
and the one has what one might consider an auto-catalysed reaction, where the
result of the brain getting bigger is to make it even easier to even bigger.

So can we identify a possible trigger point where developing
a bigger brain became more economic, triggering an auto-catalysed growth. In
general terms the appearance of language must be involved, and it is relevant to
see how far this could have been based on the simple memode model.

In fact there seem to be two potential trigger points. The
first relates to the point where the information passed from an adult to a child
is more reliable than the information that the child can learn for itself. When
this happens there are evolutionary advantages in the child bypassing the normal
learning mechanisms of the basic memode model and directly accepting what is
said as gospel. But these is already an “express learning” mechanism in any
animal's brain for handling life-threatening events – for the simple reason that
you cannot afford to learn by trial and error – and it is probably relevant that
in human teaching there is often a “carrot and stick” element in the teaching
process. Such a change in learning mechanism will significantly speed learning
and reduce the instruction time and could explain the sudden change of gear in
the way young children learn once they have got a fair grasp of language.

It is interesting to look at the cultural implications of a switch to "speed learning." In
evolutionary terms the quicker the exchange of information between parent and child the better. It helps
the process if a child asks its parents questions but in the days well before
before encyclopedias and public libraries some of the questions – such as “Why
does that butterfly have eyes on its wings?” would have been unanswerable and would have no survival value. Rather than waste time or loose authority by admitting the answer is unknown
there could be an advantage to the parent in having a stock answer to cover all such questions
– such as “the invisible spirit in the oak tree did it.” Because of the way
culture works, within a few generations a supernatural origin myth would have
come into existence. In addition, because the speed learning mechanism is related to ensuring animals avoid danger, supernatural stories which promise a choice
between an unbelievably attractive heaven and the horrors of eternal damnation
in hell would be particularly attractive. Of course the religious may not like
the fact that what they think was a “God Gene” only exists because humans evolved to accept as correct
any statement (true or false) spouted by a suitably authoritative looking
human, and that some individuals are more willing to absorb unverified (and unverifiable) information as being correct.

The second mechanism relates to “programming the mind.” The
work with CODIL, and particularly the problem solver TANTALIZE show that
something akin to the memode model can actually be used to support very complex
algorithms, although it is not obvious how a individual could create such algorithms using
simple learning techniques. On the other hand once language has advanced to the
stage of being able to say “here is a recipe for doing ...” you may only need
one person to stumble on the way to do something – and transmission of the new information processing technique becomes
cultural. Areas where some “programming” help could be relevant would be the handling of numbers (expanding to much of mathematics and formal logic) and the syntax
structures of the many different human languages.If this is the case there is no need for any special biological evolution to support such human mental activities.

This all suggests that the human brain could work using
virtually identical decision making mechanisms to an animal's brain, and that
most if not all of what seems intelligence is due to cultural factors relating
to learning technigques. This does not mean that other changes are not
significant. In this context the recent work on the
SRGAP2
gene is interesting. This gene is connected with the connections between
the neurons, the form SRGAP2a is common to chimpanzees and humans. However the
gene has duplicated itself twice with the second duplication probably been
relevant to the split between the Australophithecus and Homo
lines. The duplication of the gene appears to have introduced a step function in
the number of connections between neurons, which at the very least would have
affected the cost effectiveness of the brain – and which might, for example made
some differences in the nature of the links. Other changes associated with the
size and capacity of the brain may have also had a step effect.

8. What Next?

Of course humans are something special in terms with what
they have done on this planet, and the information they have collectively
assembled. But how far is all this due to culture and how far are humans
exceptional in purely evolutionary terms. Among the mammals we have giraffes
with very long necks, elephants with prehensile noses, bats that fly, and
legless whales which can swim deep in the sea. The purpose of this post is to
argue that from the biological point of view humans are just mammals with big
heads (in both senses of the phrase) and that demonstrate that a simple memode
model, supported by blue sky experimental research on CODIL, plus changes in
learning stimulated by language and culture, may be sufficient to explain our
intelligence.

Of course I am aware that there are very many more questions
to be answered, and outstanding problems to be solved, before the ideas can
become ether firmly established or disproved. If I was a young scientist trying
to find out how to bridge the “Black Hole” in the centre of brain research I
would be raring to go. But of course I am not. I have long retired, having given
up active scientific research years ago, in part for medically related reasons,
and I have neither the (life)time, energy or resources to follow the ideas
through to a polished conclusion. However I can still “think research” and if
anyone wants to explore the ideas posted here in more detail I am very happy to
exchange information on this blog, or in otherways brainstorm the issues.

Search This Blog

About Me

A.K.A. Hertfordshire Chris -
I am, by temperament, a scientist who likes to stand back and get an overview - rather than getting stuck in a narrow specialist area. I am particularly interested in how people process information and how we could design systems that fit into the way that people think.
Since I retired I have been very much involved in mental health service provision (but have recently retired from all committee work) and I run a web site at www.hertfordshire-genealogy.co.uk which provides help and advice for people whose ancestors lived in Hertfordshire.